Search Results (25)

Over the past two decades, Light Detection and Ranging (LiDAR) technology has evolved from early National Aeronautics and Space Administration (NASA)-led airborne laser altimetry into commercially mature systems that now underpin vegetation remote sensing across scales. Continuous advancements in laser engineering, signal processing, and complementary technologies—such as Inertial Measurement Units (IMU) and Global Navigation Satellite Systems (GNSS)—have yielded compact, cost-effective, and highly sophisticated LiDAR sensors. Concurrently, innovations in carrier platforms, including uncrewed aerial systems (UAS), mobile laser scanning (MLS), Simultaneous Localization and Mapping (SLAM) frameworks, have expanded LiDAR’s observational capacity from plot- to global-scale applications in forestry, precision agriculture, ecological monitoring, Above Ground Biomass (AGB) modeling, and wildfire science. This review synthesizes LiDAR’s cross-domain capabilities for the following: (a) quantifying vegetation structure, function, and compositional dynamics; (b) recent sensor developments encompassing ALS discrete-return ${(ALS}_D)$, and ALS full-waveform $\left({ALS}_{FW}\right)$, photon-counting LiDAR (PCL), emerging multispectral LiDAR (MSL), and hyperspectral LiDAR (HSL) systems; and (c) state-of-the-art data processing and fusion workflows integrating optical and radar datasets. The synthesis demonstrates that many LiDAR-derived vegetation metrics are inherently transferable across domains when interpreted within a unified structural framework. The review further highlights the growing role of artificial-intelligence (AI)-driven approaches for segmentation, classification, and multitemporal analysis, enabling scalable assessments of vegetation dynamics at unprecedented spatial and temporal extents. By consolidating historical developments, current methodological advances, and emerging research directions, this review establishes a comprehensive state-of-the-art perspective on LiDAR’s transformative role and future potential in monitoring and modeling Earth’s vegetated ecosystems. Full article

Forested wetlands in temperate mountain ecosystems play a critical role in carbon sequestration and biodiversity maintenance, yet their accurate delineation remains challenging due to spectral similarity with forests and anthropogenic interference. Here, we present an optimized two-stage Random Forest framework integrating 2019–2022 growing season datasets from Sentinel-1 C-SAR, ALOS-2 L-PALSAR, Sentinel-2 MSI, and Landsat-8 TIRS with environmental covariates. The methodology first applied NDBI thresholding (NDBI > 0.12) to exclude 94% of urban/agricultural areas through spectral masking, then implemented an optimized Random Forest classifier (ntree = 1200, mtry = 28) with 10-fold cross-validation, leveraging 42 features including L-band HV backscatter (feature importance = 47), Sentinel-2 SWIR (Band12; importance = 57), and land surface temperature gradients. This study pioneers a 10 m resolution forest swamp map in the Changbai Mountain wetlands, achieving 87.18% overall accuracy (Kappa = 0.84) with strong predictive performance (AUC = 0.89). Forest swamps showed robust classification metrics (PA = 80.37%, UA = 86.87%), driven by L-band SAR’s superior discriminative power (p < 0.05). Quantitative assessment demonstrated that L-band SAR increased classification accuracy in canopy penetration scenarios by 4.2% compared to optical-only approaches, while thermal-IR features reduced confusion with forests. Forested swamps occupied 229.95 km² (9% of protected areas), predominantly in transitional ecotones (720–850 m elevation) between herbaceous wetlands and forest. This study establishes that multi-sensor fusion enables operational wetland monitoring in topographically complex regions, providing a transferable framework for temperate mountain ecosystems. The dataset advances precision conservation strategies for these climate-sensitive habitats, supporting sustainable development goals targets for wetland protection through enhanced machine learning interpretability and anthropogenic interference mitigation. Full article

This study aims to address the challenges associated with rockfall assessment and monitoring, focusing on the coastal cliffs of “Navagio Shipwreck Beach” in Zakynthos. A complete time-series analysis was conducted using state-of-the-art methodologies including a 2020 survey using unmanned aerial systems (UASs) and two subsequent surveys, incorporating terrestrial laser scanning (TLS) and UAS survey techniques in 2023. Achieving high precision and accuracy in georeferencing involving direct georeferencing, the utilization of pseudo ground control points (pGCPs), and integrating post-processing kinematics (PPK) with global navigation satellite system (GNSS) permanent stations’ RINEX data is necessary for co-registering the multitemporal models effectively. For the change detection analysis, UAS surveys were utilized, employing the multiscale model-to-model cloud comparison (M3C2) algorithm, while TLS data were used in a validation methodology due to their very high-resolution model. The synergy of these advanced technologies and methodologies offers a comprehensive understanding of rockfall dynamics, aiding in effective assessment and monitoring strategies for coastal cliffs prone to rockfall risk. Full article

Given the growing threat posed by the widespread availability of unmanned aircraft systems (UASs), which can be utilised for various unlawful activities, the need for a standardised method to evaluate the effectiveness of systems capable of detecting, tracking, and identifying (DTI) these devices has become increasingly urgent. This article draws upon research conducted under the European project COURAGEOUS, where 260 existing drone detection systems were analysed, and a methodology was developed for assessing the suitability of C-UASs in relation to specific threat scenarios. The article provides an overview of the most commonly employed technologies in C-UASs, such as radars, visible light cameras, thermal imaging cameras, laser range finders (lidars), and acoustic sensors. It explores the advantages and limitations of each technology, highlighting their reliance on different physical principles, and also briefly touches upon the legal implications associated with their deployment. The article presents the research framework and provides a structural description, alongside the functional and performance requirements, as well as the defined metrics. Furthermore, the methodology for testing the usability and effectiveness of individual C-UAS technologies in addressing specific threat scenarios is elaborated. Lastly, the article offers a concise list of prospective research directions concerning the analysis and evaluation of these technologies. Full article

The lack of a solid methodology defining urban and non-urban areas has hindered accurately estimating the Surface Urban Heat Island (SUHI). This study addresses this issue by using the official national urban areas limit together with a surrounding areas classification to define three different reference classes: the urban adjacent (Ua), the future urban adjacent (FUa), and the peri-urban (PUa), consequently providing a more accurate SUHI estimation on the nine northeastern Brazilian capitals. The land surface temperature was obtained in this study using the Sentinel-3 satellite data for 2019 and 2020. Subsequently, the maximum and average SUHI and the complementary indexes, specifically the Urban Thermal Field Variation Index (UTFVI) and the Thermal Discomfort Index (TDI), were calculated. The UTFVI expresses how harmful the eco-environmental spaces are, with a very strong SUHI for three capitals. In addition, the TDI, with values between 24.6–28.8 °C, expresses the population’s thermal comfort, with six capitals showing a very hot TDI. These findings highlight the need for strategies to mitigate the effects of the SUHI and ensure the population’s thermal comfort. Therefore, this study provides a better SUHI understanding and comparison for the Brazilian northeastern region, which has diverse areas, populations, and demographic variations. Full article

Background: The “Liquid Biopsy” has become a powerful tool for cancer research during the last decade. Circulating cell-free DNA (cfDNA) that originates from tumors has emerged as one of the most promising analytes. In contrast to plasma-derived cfDNA, only a few studies have investigated urinary cfDNA. One reason might be rapid degradation and hence inadequate concentrations for downstream analysis. In this study, we examined the stability of cfDNA in urine using different methods of preservation under various storage conditions. Methodology: To mimic patient samples, a pool of healthy male and female urine donors was spiked with a synthetic cfDNA reference standard (fragment size 170 bp) containing the T790M mutation in the EGFR gene. Spiked samples were preserved with three different buffers and with no buffer over four different storage periods (0 h; 4 h; 12 h; 24 h) at room temperature vs. 4 °C. The preservatives used were Urinary Analyte Stabilizer (UAS, Novosanis, Wijnegem, Belgium), Urine Conditioning Buffer (UCB, Zymo, Freiburg, Germany) and a self-prepared buffer called “AlloU”. CfDNA was extracted using the QIAamp MinElute ccfDNA Mini Kit (Qiagen, Hilden, Germany). CfDNA concentration was measured using the Qubit™ 4 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). Droplet digital PCR (ddPCR) was used for detection and quantification of the T790M mutation. Results: Almost no spiked cfDNA was recoverable from samples with no preservation buffer and the T790M variant was not detectable in these samples. These findings indicate that cfDNA was degraded below the detection limit by urinary nucleases. Stabilizing buffers showed varying efficiency in preventing this degradation. The most effective stabilizing buffer under all storage conditions was the UAS, enabling adequate recovery of the T790M variant using ddPCR. Conclusion: From a technical point of view, stabilizing buffers and adequate storage conditions are a prerequisite for translation of urinary cfDNA diagnostics into clinical routine. Full article

The accurate and detailed measurement of the vertical temperature, humidity, pressure, and wind profiles of the atmosphere is pivotal for high-resolution numerical weather prediction, the determination of atmospheric stability, as well as investigation of small-scale phenomena such as urban heat islands. Traditional approaches, such as weather balloons, have been indispensable but are constrained by cost, environmental impact, and data sparsity. In this article, we investigate uncrewed aerial systems (UASs) as an innovative platform for in situ atmospheric probing. By comparing data from a drone-mounted semiconductor temperature sensor (TMP117) with traditional radiosonde measurements, we spotlight the UAS-collected atmospheric data’s accuracy and such system suitability for atmospheric surface layer measurement. Our research encountered challenges linked with the inherent delays in achieving ambient temperature readings. However, by applying specific data processing techniques, including smoothing methodologies like the Savitzky–Golay filter, iterative smoothing, time shift, and Newton’s law of cooling, we have improved the data accuracy and consistency. In this article, 28 flights were examined and certain patterns between different methodologies and sensors were observed. Temperature differentials were assessed over a range of 100 m. The article highlights a notable accuracy achievement of 0.16 ± 0.014 °C with 95% confidence when applying Newton’s law of cooling in comparison to a radiosonde RS41’s data. Our findings demonstrate the potential of UASs in capturing accurate high-resolution vertical temperature profiles. This work posits that UASs, with further refinements, could revolutionize atmospheric data collection. Full article

The genus Corynebacterium is frequently found in the female urinary microbiome (FUM). In-depth characterization of Corynebacterium at the species level has been barely exploited. During ongoing FUM research studies, eight strains (c8Ua_144^T, c8Ua_172^T, c8Ua_174^T, c8Ua_181^T, c9Ua_112^T, c19Ua_109^T, c19Ua_121^T, and c21Ua_68^T) isolated from urine samples of healthy women or diagnosed with overactive bladder could not be allocated to any valid Corynebacterium species. In this work, we aimed to characterize these strains based on a polyphasic approach. The strains were Gram stain positive, rod to coccoid shaped, nonmotile, catalase positive, and oxidase negative. Phylogenetic analysis based on 16S rRNA and rpoB gene sequences indicated that all strains belonged to the genus Corynebacterium. The average nucleotide identity and digital DNA–DNA hybridization values among the genomes of the above eight strains and closely related type strains of the Corynebacterium genus were <95 (74.1%–93.9%) and <70% (22.2%–56.5%), respectively. Mycolic acids were identified in all strains. MK-8(H2) and/or MK-9(H2) were identified as the major menaquinones. The polar lipids’ pattern mostly consisted of diphosphatidylglycerol, phosphatidylglycerol, and glycophospholipids. The major fatty acid was C_18:1ω9c. Corynebacterium lehmanniae (c8Ua_144^T = DSM 113405^T = CCP 74^T), Corynebacterium meitnerae (c8Ua_172^T = DSM 113406^T = CCP 75^T), Corynebacterium evansiae (c8Ua_174^T = DSM 113407^T = CCP 76^T), Corynebacterium curieae (c8Ua_181^T = DSM 113408^T = CCP 77^T), Corynebacterium macclintockiae (c9Ua_112^T = DSM 113409^T = CCP 78^T), Corynebacterium hesseae (c19Ua_109^T = DSM 113410^T= CCP 79^T), Corynebacterium marquesiae (c19Ua_121^T = DSM 113411^T = CCP 80^T), and Corynebacterium yonathiae (c21Ua_68^T = DSM 113412^T = CCP 81^T) are proposed. This study evidenced that commonly used methodologies on FUM research presented limited resolution for discriminating Corynebacterium at the species level. Future research studying the biological mechanisms of the new Corynebacterium species here described may shed light on their possible beneficial role for healthy FUM. Full article

Ursolic acid (UA) is a plant-derived molecule with relevant anti-aging activity, which makes this molecule a potential functional active ingredient in cosmetic formulations. The main objectives of this study were to optimize the UA extraction process from Annurca apple (AA) with sunflower oil as a lyophilic food-grade solvent using Response Surface Methodology (RSM) to determine the potential cosmetic application of the obtained extract. The results of RSM analysis showed a maximum UA yield of 784.40 ± 7.579 (μg/mL) obtained under the following optimized conditions: sunflower oil as extraction solvent, 68.85 °C as extraction temperature, and 63 h as extraction time. The HPLC-DAD-HESI-MS/MS analysis performed on the extract obtained under these conditions, named Optimized Annurca Apple Oleolyte (OAAO), led to the identification of twenty-three phenolic and terpenoid molecules and the quantification of eight of them. To explore the biological properties of OAAO, the in vitro antioxidant activity was evaluated by DPPH, ABTS, and FRAP assays, resulting in 16.63 ± 0.22, 5.90 ± 0.49, and 21.72 ± 0.68 μmol Trolox equivalent/g extract, respectively. Moreover, the permeation study has shown that OAAO may be considered a safe and functional ingredient in potential cosmetic formulations. Full article

Extracting ursolic acid (UA) from plant resources using organic solvents is incompatible with food applications. To address this, in this study, 15 edible hydrophobic deep eutectic solvents (HDESs) were prepared to extract UA from apple peel, the extraction conditions were optimized, and the optimization strategies were compared. It was found that the solubility of UA in the HDESs can be 9 times higher than the traditional solvent such as ethanol. The response surface optimization concluded that temperature had the greatest effect on the extraction and the optimized test conditions obtained as follows: temperature of 49 °C, time of 32 min, solid–liquid ratio of 1:16.5 g/mL, respectively. Comparing the response surface methodology (RSM) and artificial neural networks (ANN), it was concluded that ANN has more accurate prediction ability than RSM. Overall, the HDESs are more effective and environmentally friendly than conventional organic solvents to extract UA. The results of this study will facilitate the further exploration of HDES in various food and pharmaceutical applications. Full article

Aim: To perform a systematic review and meta-analysis of existing literature to evaluate the incidence of cranial ultrasound abnormalities (CUAs) amongst moderate to late preterm (MLPT) and term infants, affected by fetal growth restriction (FGR) or those classified as small for gestational age (SGA). Methods: A systematic review methodology was performed, and Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) statement was utilised. Descriptive and observational studies reporting cranial ultrasound outcomes on FGR/SGA MLPT and term infants were included. Primary outcomes reported was incidence of CUAs in MLPT and term infants affected by FGR or SGA, with secondary outcomes including brain structure development and growth, and cerebral artery Dopplers. A random-effects model meta-analysis was performed. Risk of Bias was assessed using the Newcastle-Ottawa scale for case–control and cohort studies, and Joanna Briggs Institute Critical Appraisal Checklist for studies reporting prevalence data. GRADE was used to assess for certainty of evidence. Results: Out of a total of 2085 studies identified through the search, seventeen were deemed to be relevant and included. Nine studies assessed CUAs in MLPT FGR/SGA infants, seven studies assessed CUAs in late preterm and term FGR/SGA infants, and one study assessed CUAs in both MLPT and term FGR/SGA infants. The incidence of CUAs in MLPT, and late preterm to term FGR/SGA infants ranged from 0.4 to 33% and 0 to 70%, respectively. A meta-analysis of 7 studies involving 168,136 infants showed an increased risk of any CUA in FGR infants compared to appropriate for gestational age (AGA) infants (RR 1.96, [95% CI 1.26–3.04], I² = 68%). The certainty of evidence was very low due to non-randomised studies, methodological limitations, and heterogeneity. Another meta-analysis looking at 4 studies with 167,060 infants showed an increased risk of intraventricular haemorrhage in FGR/SGA infants compared to AGA infants (RR 2.40, [95% CI 2.03–2.84], I² = 0%). This was also of low certainty. Conclusions: The incidence of CUAs in MLPT and term growth-restricted infants varied widely between studies. Findings from the meta-analyses suggest the risk of CUAs and IVH may indeed be increased in these FGR/SGA infants when compared with infants not affected by FGR, however the evidence is of low to very low certainty. Further specific cohort studies are needed to fully evaluate the benefits and prognostic value of cranial ultrasonography to ascertain the need for, and timing of a cranial ultrasound screening protocol in this infant population, along with follow-up studies to ascertain the significance of CUAs identified. Full article

Technological progress in Virtual Reality (VR) and Unmanned Aerial Systems (UASs) offers great advantages in the field of cartography and particularly in the geovisualization of spatial data. This paper investigates the correlation between UAS flight characteristics for data acquisition and the quality of the derived maps and 3D models of geological monuments for VR geovisualization in different scales and timeframes. In this study, we develop a methodology for mapping geoheritage monuments based on different cartographic scales. Each cartographic scale results in diverse orthophotomaps and 3D models. All orthophotomaps and 3D models provide an optimal geovisualization, combining UAS and VR technologies and thus contributing to the multitemporal 3D geovisualization of geological heritage on different cartographic scales. The study area selected was a fossilite ferrous site located in Lesvos Geopark, UNESCO. The study area contains a fossil site surrounding various findings. The three distinct scales that occur are based on the object depicted: (i) the fossilite ferrous site (1:120), (ii) the fossil root system (1:20), and (iii) individual fossils (≥1:10). The methodology followed in the present research consists of three main sections: (a) scale-variant UAS data acquisition, (b) data processing and results (2D–3D maps and models), and (c) 3D geovisualization to VR integration. Each different mapping scale determines the UAS data acquisition parameters (flight pattern, camera orientation and inclination, height of flight) and defines the resolution of the 3D models to be embedded in the VR environment. Due to the intense excavation of the study area, the location was spatiotemporally monitored on the cartographic scale of 1:120. For the continuous monitoring of the study area, four different UASs were also used. Each of them was programmed to fly and acquire images with a constant ground sampling distance (GSD). The data were processed by image-based 3D modeling and computer vision algorithms from which the 3D models and orthophotomaps were created and used in the VR environment. As a result, a VR application visualizing multitemporal data of geoheritage monuments across three cartographic scales was developed. Full article

Detecting newly established invasive plants is key to prevent further spread. Traditional field surveys are challenging and often insufficient to identify the presence and extent of invasions. This is particularly true for wetland ecosystems because of difficult access, and because floating and submergent plants may go undetected in the understory of emergent plants. Unpiloted aerial systems (UAS) have the potential to revolutionize how we monitor invasive vegetation in wetlands, but key components of the data collection and analysis workflow have not been defined. In this study, we conducted a rigorous comparison of different methodologies for mapping invasive Emergent (Typha × glauca (cattail)), Floating (Hydrocharis morsus-ranae (European frogbit)), and Submergent species (Chara spp. and Elodea canadensis) using the machine learning classifier, random forest, in a Great Lakes wetland. We compared accuracies using (a) different spatial resolutions (11 cm pixels vs. 3 cm pixels), (b) two classification approaches (pixel- vs. object-based), and (c) including structural measurements (e.g., surface/canopy height models and rugosity as textural metrics). Surprisingly, the coarser resolution (11 cm) data yielded the highest overall accuracy (OA) of 81.4%, 2.5% higher than the best performing model of the finer (3 cm) resolution data. Similarly, the Mean Area Under the Receiving Operations Characteristics Curve (AUROC) and F1 Score from the 11 cm data yielded 15.2%, and 6.5% higher scores, respectively, than those in the 3 cm data. At each spatial resolution, the top performing models were from pixel-based approaches and included surface model data over those with canopy height or multispectral data alone. Overall, high-resolution maps generated from UAS classifications will enable early detection and control of invasive plants. Our workflow is likely applicable to other wetland ecosystems threatened by invasive plants throughout the globe. Full article

Background: Omalizumab is the best treatment for patients with chronic spontaneous urticaria (CSU). Machine learning (ML) approaches can be used to predict response to therapy and the effectiveness of a treatment. No studies are available on the use of ML techniques to predict the response to Omalizumab in CSU. Methods: Data from 132 CSU outpatients were analyzed. Urticaria Activity Score over 7 days (UAS7) and treatment efficacy were assessed. Clinical and demographic characteristics were used for training and validating ML models to predict the response to treatment. Two methodologies were used to label the data based on the response to treatment (UAS7 ≥ 6): (A) at 1, 3 and 5 months; (B) classifying the patients as early responders (ER), late responders (LR) or non-responders (NR) (ER: UAS 7 ≥ 6 at first month, LR: UAS 7 ≥ 6 at third month, NR: if none of the previous conditions occurred). Results: ER were predominantly characterized by hypertension, while LR mainly suffered from asthma and hypothyroidism. A slight positive correlation (R² = 0.21) was found between total IgE levels and UAS7 at 1 month. Variable Importance Analysis (VIA) reported D-dimer and C-reactive proteins as the key blood tests for the performance of learning techniques. Using methodology (A), SVM (specificity of 0.81) and k-NN (sensitivity of 0.8) are the best models to predict LR at the third month. Conclusion: k-NN plus the SVM model could be used to identify the response to treatment. D-dimer and C-reactive proteins have greater predictive power in training ML models. Full article

The proliferation of Unmanned Aerial System (UAS) capabilities in the commercial sector is posing potentially significant threats to the traditional perimeter defense of civilian and military facilities. Commercial Off-The-Shelf (COTS) UAS are small, cheap, and come with multiple types of functions which have growing interest among hobbyists. This has prompted the need for facility commanders to have a methodology to conduct quick evaluation and analysis of the facility and the existing Counter-Unmanned Aerial System (CUAS)’s effectiveness. This research proposes a methodology that follows a systems engineering perspective to provide a step-by-step process in conducting evaluation and analysis by employing Model-Based Systems Engineering (MBSE) tools to understand the CUAS’s effectiveness and limitations. The methodology analyzes the CUAS’s operating environment and effects of the dominant factors and impacts that CUAS may pose to other stakeholders (e.g., adjacent allied forces, civilians, etc.) within the area of operation. We then identify configuration candidates for optimizing the CUAS’s performance to meet the requirements of the stakeholders. A case study of a hypothetical airport with existing CUAS is presented to demonstrate the usability of the methodology, explore the candidates, and justify the implementation of a candidate that fits the facility and the stakeholders’ requirements. Full article